Bringing Financial Expertise Into Software

Megan Hale’s spreadsheets had helped hundreds of founders plan their finances, but turning them into software meant teaching a product to make recommendations she had spent years explaining herself. Coura helped her define and build the first release, which generated $30,000 in launch-day software sales.

DreamMoney founder Megan Hale had spent years helping entrepreneurs build businesses that could support both their work and their lives. After starting her career as a psychotherapist, she became a business strategist and created the DreamMoney Method in 2017. Through her work with women building multi-six- and seven-figure businesses, she saw how closely decisions about owner pay and hiring were tied to what each person needed their business to provide.

Most financial frameworks give business owners one set of rules to follow. The DreamMoney Method takes a different view. It offers recommended ranges for a healthy, profitable business. But it also recognizes that the right numbers depend on what season a business is in. A founder in a startup season needs something different from one who is intentionally scaling or working to stabilize. Each of those seasons comes with trade-offs. Rather than making those calls for business owners, the method helps them see the benefits and risks of each choice so they can make the call themselves.

Hundreds of founders had used her spreadsheets to work through those decisions. But the spreadsheets were only part of the equation. When Megan reviewed the numbers with a client, she could use her experience and understanding to explain a recommendation. She could also adjust it to account for something the spreadsheet didn’t capture. A customer using software on their own would need that guidance built into the experience.

That was the challenge she brought to Coura. How could she bring the value of the method she developed and knew intimately into a product that could consistently respond as she would? It needed to do so even when she wasn’t there to interpret the results.

At a glance

Client: DreamMoney
Founder: Megan Hale, MA
Focus: Financial leadership platform for solo CEOs
Engagement: Product strategy, UX/UI design, MVP development, and founder advisory

Engagement outcomes:

• A first release focused on online business owners
• A written guide that turned Megan’s judgment into consistent, repeatable rules
• $30,000 in launch-day software sales
• A product customers could use independently while Megan continued developing the larger platform

Where the planning had left off

Before working with Coura, Megan had invested $17,500 in planning the platform. That included $5,000 for wireframes, $7,500 for work with a chief product officer, and $5,000 in early technical guidance. Those engagements helped her visualize the product and explore the customer journey. But many of the decisions that would take this from idea to product were still unresolved.

Megan knew which part of her IP she wanted to bring into software first. The questions now concerned how it should behave. If a customer’s expenses fell outside an expected range, what should the product recommend? And if the customer disagreed, how much freedom should they have to change the plan?

“I was looking for somebody who could really expand upon all of the clarity work that I had already done and also fill in the gaps,” Megan said.

A mutual connection introduced her to Amodhi and Coura. As they worked through the method together, the team helped her answer those questions clearly enough that the product could act on them the same way every time.

“If I could go back and do it all over again, I wish that I would have just gone straight to work with Coura,” Megan said. “I got so much more in that process.”

Giving the first release a clear customer

Before the team could settle every calculation, they needed to decide whose finances the first release would support. Megan’s experience covered businesses with very different operating costs. A business paying rent for a physical location would need different recommendations from one operating online.

A business with a storefront doesn’t look like a business run from a laptop, and the method didn’t pretend it should. Megan raised this with a brick-and-mortar client she had spoken with that morning. The client’s rent, utilities, and other operating costs came to $12,000 a month. By the usual ranges, those numbers might look like a problem. But in reality, they were simply the cost of running that kind of business. Holding a brick-and-mortar business owner to guidance designed for an online business wouldn’t have helped her make better decisions.

That conversation reinforced DreamMoney’s emphasis on understanding the kind of business an owner runs before offering financial guidance. Rather than stretching one set of recommendations across every business model, Megan and Coura focused the first release on online business owners, so the guidance those customers received would actually fit their reality. The larger DreamMoney platform would be designed to grow into the needs of other kinds of businesses over time.

Guiding decisions rather than making them

Many financial frameworks treat their targets as pass or fail, with a number either in range or it’s a problem. DreamMoney treats its recommended ranges as a picture and reference point of what a healthy, profitable business looks like, and allows the owner to decide how closely to follow it. Carrying that philosophy into software was one of the most important things the first release had to get right.

Customers needed room to make choices that differed from the method’s recommendations. The product needed to let them compare their current finances with the suggested plan, then adjust inputs to see how their business expenses and household costs affected the revenue they needed to earn.

Similarly, when a choice fell outside the recommended range, the product shouldn’t outright block it or flag it as wrong. Instead, it would direct the customer’s attention to what that choice would make possible and what it would put at risk. That approach preserved the guidance Megan would offer in a client session while leaving the business owner responsible for the decision.

Megan also wanted customers to feel comfortable trying different plans. When the team reviewed how someone would accept a recommendation, she asked whether they could return to their original numbers after saving. A customer still learning the calculator might hesitate if a choice felt permanent. Caroline, Coura’s COO at the time, suggested using “modify” rather than “accept” for the button, allowing the team to explore ways to let users return to their earlier figures.

Getting Megan’s instincts into the calculations

Choosing the first audience narrowed the problem, but it didn’t answer every question about how the method should work. Megan’s spreadsheets showed the math, but much of the reasoning behind her recommendations came from years of client conversations. In a session, she could weigh a founder’s circumstances and adjust on the spot. The product needed rules firm enough to deliver consistent answers and flexible enough to fit real, messy businesses.

This was the hardest part of the build.

Megan had seen owners put their own pay last. In one mother-and-daughter business, the owners were paying themselves roughly $900 a month while spending substantially more on individual team members. Experiences like that shaped a rule for the calculator: the recommended plan could not reduce owner pay below the amount the customer needed to support their household. If the numbers didn’t fit, the plan had to find room somewhere else.

Coura worked through sample financial situations with her and wrote those rules down in a calculation guide. As the DreamMoney product came together, that guide became the way to check whether it was responding the way Megan would.

Some rules only surfaced when the team asked what should happen next. If a plan had money left over, where would Megan put it? What if that category was already at the top of its recommended range? Answering those questions meant examining choices she usually made without stopping to explain them. Megan asking “Why am I making the decisions that I am?” and  “How am I prioritizing this in my head?” in sessions with Coura turned instincts she’d never needed to explain into priorities the product could follow.

Making the build manageable

Megan was still running her existing business, so having the team bring her something to react to made the process easier.

“Here’s my IP. Y’all mold this and make it something,” Megan joked. “And then they brought me ideas [so that] I had something to respond to.”

If a calculation differed from the recommendation she would give a client, they worked back through her reasoning and adjusted the rules. Those conversations also exposed situations that needed more consideration than the first release could support.

Reviewing the sample calculations meant considering what a recommendation would ask someone to do. Megan pointed out that cutting a hypothetical team budget from $40,000 a month to $25,000 could mean letting people go. Later, when a worked example suggested reducing business expenses by nearly half, she challenged the result against the more gradual reduction approach they had been discussing. Getting a category into range wasn’t enough; she also wanted to account for how much change the recommendation required of the owner.

That kind of tradeoff is exactly what the DreamMoney Method is built to recognize. A business owner deliberately taking lower pay and spending heavily on staff to scale isn’t necessarily making a mistake. They’re in a different season, and they need different guidance from someone who is just starting out or working to stabilize. Supporting that full range of growth stages would take more than the first release could handle, so the team documented where the first product’s recommendations applied and set stage-specific guidance aside for later versions of the platform.

By the time the guide was complete, decisions that had once depended on Megan’s judgment in a client session could be made consistently, without her in the room.

Keeping changes in proportion to the launch

DreamMoney was preparing to launch in front of roughly 100 people, and one especially tricky part of the math took sustained effort to get right. As Megan saw the product take shape, she also saw opportunities to improve it, and the team had to weigh those ideas against the work still needed before launch.

Weekly standups gave them a place to make those decisions together. In some cases, simplifying the experience also reduced what had to be built. Advertising, for instance, became part of general business expenses rather than a separate budget line.

The decision came from thinking about a real business owner. Megan pictured someone who wanted to spend $250 a month on Facebook ads to grow. If advertising had its own separate bucket, that owner would have to pull the money from somewhere else, even if they were already running lean. Megan knew that kind of squeeze makes people feel like they’re failing at their finances when they’re actually making a smart investment. By treating advertising as a normal business expense, the DreamMoney product would simply show the owner how much revenue they needed to cover it, so growing the business wouldn’t come at the cost of everything else.

Not every idea could make it into the launch.

When Megan wanted to simplify how recommendations were displayed, the team kept the original design in place while they figured out whether they could make the change well in the time remaining. The goal was a product customers could trust on day one, not one that tried to do everything at once.

“Amodhi is just so skilled at keeping the boundaries on and really making sure that you’re going to hit your milestones and your timelines while still balancing the need for a quality product,” Megan said.

A $30,000 Launch Day

DreamMoney launched its first release to a live audience and generated $30,000 in software sales that day. With the product in customers’ hands, Megan could continue selling it while developing the larger platform she had envisioned.

If customers already rely on your expertise, bringing it into software means working through the questions you currently answer for them. Coura can help define what the first product should handle and document how it needs to respond.

Reach out today.